PROJECT SUMMARY Importance: There were over 700,000 end-stage renal disease (ESRD) patients receiving renal replacement therapy dialysis in the United States at the end of 2015. ESRD has a substantial impact on mortality, morbidity, health care cost and quality of life. The preferred therapy, kidney transplantation, is in relatively short supply; e.g., less than 19,000 kidney transplants occurred in the U.S. in 2015, with about 83,000 patients remaining on the wait list at year end. Problem: Given that mortality and hospitalization rates are quite high among ESRD patients, flexible, broadly applicable and easily implementable methods of analysis are required for modeling hospitalization, death, and the two processes simultaneously. Existing methods either fail to target quantities of interest in Aims 1-3 (below), or do so using strong assumptions which limit their applicability. Overall Objective: The overarching goal of this project is to develop survival analysis methodology to support analyses that will produce a deeper understanding of morbidity and mortality patterns among ESRD patients. Such increased understanding should lead to improvements in renal replacement therapy and, in turn, improved survival and quality of life among ESRD patients. Target Audience: With respect to methodology, the target audience includes biostatisticians, particularly practitioners studying ESRD and other chronic illnesses. Results based on the proposed analyses would be of interest to nephrologists, transplant surgeons and ESRD patients. Products: Novel and innovative methods for the analysis of survival and recurrent event data. Specific Aim 1: Methods for alternating recurrent event data Methods will be developed for analyzing alternating gap time data (e.g., time to readmission; length of hospital stay). They will be applied to the Dialysis Outcomes and Practice Patterns Study (DOPPS). Specific Aim 2: Simultaneously modeling state prevalence and survival We will develop methods for jointly modeling state prevalence probability and survival. The methods will be applied to SRTR data to analyze the probability of being active on the kidney transplant wait list. Specific Aim 3: Instrumental variable (IV) methods based on restricted mean survival time (RMST) Methods to estimate causal treatment effects on survival through IV analysis will be developed. The methods will be applied to compare survival by dialytic modality using USRDS data. For each Aim, the methods will be easily implementable since user-friendly software (SAS, R) will be developed and made available online.